Method and system for image processing

ABSTRACT

Various aspects of a system and method for image processing are disclosed herein. The method, implemented in an image-processing device, comprises computation of a plurality of boundary-connectedness values associated with a plurality of regions in a plurality of Boolean maps. The plurality of Boolean maps corresponds to a plurality of color channels of an image. The plurality of boundary-connectedness values associated with the plurality of regions is compared with a pre-specified threshold value. A first set of regions is identified from the plurality of regions in the plurality of Boolean maps as a set of foreground regions, based on the comparison.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

None.

FIELD

Various embodiments of the disclosure relate to a method and system forimage processing. More specifically, various embodiments of thedisclosure relate to a method and a system for image processing toidentify salient objects.

BACKGROUND

With recent advancements in the field of computer vision and videoprocessing, various models have been proposed for automatic and/orcomputational identification of salient objects in an image and/or avideo stream. The identification of salient objects has variousapplications in the field of video surveillance, image retargeting,video summarization, robot control, navigation assistance, objectrecognition, adaptive compression, and/or the like. Further,identification of salient objects is useful in image processingtechniques, such as auto-focus algorithms, wherein detection of a focusarea is performed automatically for video and/or image-capturingdevices.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of described systems with some aspects of the presentdisclosure, as set forth in the remainder of the present application andwith reference to the drawings.

SUMMARY

A method and a system for image processing is provided substantially asshown in, and/or described in connection with, at least one of thefigures, as set forth more completely in the claims.

These and other features and advantages of the present disclosure may beappreciated from a review of the following detailed description of thepresent disclosure, along with the accompanying figures in which likereference numerals refer to like parts throughout.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that illustrates a network environment, inaccordance with an embodiment of the disclosure.

FIG. 2 is a block diagram that illustrates various components of animage-processing device, in accordance with an embodiment of thedisclosure.

FIG. 3 illustrates an exemplary scenario for implementation of thedisclosed method and system for image processing, in accordance with anembodiment of the disclosure.

FIG. 4 depicts a flowchart that illustrates a method for imageprocessing, in accordance with an embodiment of the disclosure.

DETAILED DESCRIPTION

A salient object may be identified, based on detection ofregion-of-interest (or region of attention) of a viewer. Thisregion-of-interest may correspond to at least one of the foregroundobjects within a scene. Most computer vision models require a set ofbasic visual characteristics to detect such salient objects. The set ofbasic visual characteristics may include color contrast, intensity,orientation, texture, motion, spatial distance, and/or the like.

Various saliency detection approaches, such as a Boolean map saliency(BMS) approach, have been developed to remove non-salient backgroundobjects. The BMS approach leverages global topological cues that areknown, to help in perceptual figure-ground segregation. The globaltopological cues may be based on one or more factors, such assurroundedness. The essence of surroundedness is the enclosure oftopological relationship between a figure (such as an object), and aground (such as foreground or background of an image). The relationshipbetween the figure and ground is well defined and invariant to varioustransformations. Based on the surroundedness factor, all regions thattouch one or more borders are removed from the Boolean maps thatcorrespond to the image or video frame. However, the removal of all suchregions that touch the one or more borders may decrease the performanceof the BMS approach in case the removed regions correspond to foregroundobjects. Hence, there is a need for a technique that significantlyimproves the saliency results of the BMS approach.

The following described implementations may be found in the disclosedmethod and system for image processing. Exemplary aspects of thedisclosure may include a method to compute, by an image-processingdevice, a plurality of boundary-connectedness values associated with aplurality of regions in a plurality of Boolean maps. In accordance withan embodiment, the plurality of Boolean maps may correspond to aplurality of color channels of an image. The method may includecomparison of the plurality of boundary-connectedness values associatedwith the plurality of regions with a pre-specified threshold value.Based on the comparison, the method may include identification of atleast a first set of regions of the plurality of regions as a set offoreground regions.

In accordance with an embodiment, a second set of regions of theplurality of regions may be identified as a set of background regions,based on the comparison. The identified first set of regions may beretained and the identified second set of regions may be removed fromthe plurality of Boolean maps to generate a plurality of processedBoolean maps.

In accordance with an embodiment, each of the plurality ofboundary-connectedness values associated with the plurality of regionsmay be computed from a ratio of a count of pixels (of the correspondingregion that touches one or more borders of the plurality of Booleanmaps) and a square root of total count of pixels of the correspondingregion. In accordance with an embodiment, one or moreboundary-connectedness values associated with the first set of regionsare less than or equal to the pre-specified threshold value. Further,one or more boundary-connectedness values associated with the second setof regions may exceed the pre-specified threshold value.

In accordance with an embodiment, the image may be a de-correlated imagethat may include one or more regions. The one or more regions maycorrespond to background or foreground objects. The plurality of regionsof the one or more regions touches one or more borders of the image.Further, another plurality of regions of the one or more regions doesnot touch the one or more borders of the image.

In accordance with an embodiment, the method may comprise generation ofthe plurality of Boolean maps. The Boolean maps may be generated foreach color channel of the plurality of color channels of the image andeach threshold value of a set of threshold values, based on a set ofbinary values. In accordance with an embodiment, the set of binaryvalues may be determined, based on a comparison of pixel intensity withthe threshold value at each pixel location of the image.

In accordance with an embodiment, the method may further comprise acomputation of the set of threshold values of each color channel of theplurality of color channels. The computation of the set of thresholdvalues may be based on a step size, a minimum pixel value, and a maximumpixel value of the corresponding color channel. The step size may bebased on a count of bits that represents pixel values of the pluralityof color channels.

In accordance with an embodiment, the method may further comprisegeneration of a saliency map. The generation of the saliency map maycomprise addition of the plurality of processed Boolean maps of theplurality of color channels. The method may further comprisenormalization of the added plurality of processed Boolean maps.

In accordance with an embodiment, the method may comprise detection of aregion-of-interest that may correspond to a salient region in thegenerated saliency map. In accordance with an embodiment, the detectionof the region-of-interest may be based on one or more saliencyparameters that correspond to the first set of regions identified as theset of foreground regions in the image. In such a case, the one or moresaliency parameters are based on intensity values of the detectedsalient regions that exceed a threshold intensity value.

FIG. 1 is a block diagram that illustrates a network environment, inaccordance with an embodiment of the disclosure. With reference to FIG.1, there is shown a network environment 100. The network environment 100may include an image-processing device 102, a display screen 104, aplurality of cloud-based resources 106, and a communication network 108.The image-processing device 102 may comprise the display screen 104. Theimage-processing device 102 may be communicatively coupled to theplurality of cloud-based resources 106, via the communication network108.

The image-processing device 102 may comprise suitable logic, circuitry,interfaces, and/or code that may be configured to process an image or avideo frame. The image or video frame may be processed foridentification of a set of foreground regions that may touch one or moreborders of the image or video frame. The image-processing device 102 maybe further configured to detect one or more salient objects from theprocessed image or video frame. The image-processing device 102 may befurther configured to adjust auto-focus, based on the one or moresalient objects detected in the processed image or video frame. Examplesof the image-processing device 102 may include, but are not limited to,a smartphone, a camera, a tablet computer, a laptop, and/or a wearableelectronic device.

The display screen 104 may comprise suitable circuitry and/or interfacesthat may be configured to display the image or video frame. The displayscreen 104 may be further configured to display the one or more salientobjects, on which the auto-focus is adjusted by the image-processingdevice 102. The display screen 104 may be realized through several knowntechnologies, such as but are not limited to, Liquid Crystal Display(LCD) display, Light Emitting Diode (LED) display, and/or Organic LED(OLED) display technology.

The plurality of cloud-based resources 106 may comprise one or moreservers that may provide image data to one or more subscribed electronicdevices, such as the image-processing device 102. The plurality ofcloud-based resources 106 may be implemented by use of severaltechnologies that are well known to those skilled in the art. The one ormore servers from the plurality of cloud-based resources 106, may beassociated with a single or multiple service providers. Examples of theone or more servers may include, but are not limited to, Apache™ HTTPServer, Microsoft® Internet Information Services (IIS), IBM® ApplicationServer, Sun Java™ System Web Server, and/or a file server.

The communication network 108 may include a medium through which theimage-processing device 102 may communicate with the one or moreservers, such as the plurality of cloud-based resources 106. Examples ofthe communication network 108 may include, but are not limited to, aDedicated Short-Range Communication (DSRC) network, a Mobile Ad-HocNetwork (MANET), a Vehicular Ad-Hoc Network (VANET), an IntelligentVehicular Ad-Hoc Network (InVANET), an Internet-Based Mobile Ad-HocNetwork (IMANET), a Wireless Sensor Network (WSN), a Wireless MeshNetwork (WMN), the Internet, a cellular network, such as a Long-TermEvolution (LTE) network, a cloud network, a Wireless Fidelity (Wi-Fi)network, and/or a Wireless Local Area Network (WLAN). Various devices inthe network environment 100 may be configured to connect to thecommunication network 108, in accordance with various wirelesscommunication protocols. Examples of such wireless communicationprotocols may include, but are not limited to, IEEE 802.11, 802.11p,802.15, 802.16, 1609, Worldwide Interoperability for Microwave Access(Wi-MAX), Wireless Access in Vehicular Environments (WAVE), cellularcommunication protocols, Transmission Control Protocol and InternetProtocol (TCP/IP), User Datagram Protocol (UDP), Hypertext TransferProtocol (HTTP), Long-term evolution (LTE), File Transfer Protocol(FTP), ZigBee, EDGE, Infrared (IR), and/or Bluetooth (BT) communicationprotocols.

In operation, the image-processing device 102 may be configured toreceive an image or a video frame from the plurality of cloud-basedresources 106, via the communication network 108. In accordance with anembodiment, the image-processing device 102 may be configured to receivethe image or video frame from an image-capturing unit (described in FIG.2), installed within or communicatively coupled with theimage-processing device 102. In accordance with an embodiment, theimage-processing device 102 may be configured to retrieve a pre-storedimage or video frame from a local memory. The image or video frame mayinclude one or more background and foreground objects.

In accordance with an embodiment, the image-processing device 102 may beconfigured to perform de-correlation of the plurality of color channelsof the image or video frame. The de-correlation of the plurality ofcolor channels of the image or video frame may reduce cross-correlationwithin the plurality of color channels. The image-processing device 102may apply one or more de-correlation or whitening techniques known inthe art, such as a matched linear filter, to perform such de-correlationof the plurality of color channels.

In accordance with an embodiment, the image-processing device 102 may beconfigured to determine a set of threshold values for each color channelof the plurality of color channels. Each threshold value from the set ofthreshold values (that corresponds to each color channel) may range froma minimum pixel value to a maximum pixel value. In accordance with anembodiment, each threshold value from the set of threshold values mayfurther depend on a step size. The step size may be based on a count ofbits that represents pixel values of one or more pixels of thecorresponding color channel.

In accordance with an embodiment, the image-processing device 102 may beconfigured to determine pixel values of the one or more pixels in eachcolor channel of the de-correlated image. The image-processing device102 may be further configured to compare the determined pixel values ofthe one or more pixels, with each threshold value from the set ofthreshold values that correspond to the color channel. Further, theimage-processing device 102 may be configured to assign a firstpre-defined binary value, such as “1”, to each of the one or more pixellocations, whose pixel values exceed the corresponding threshold valuesfrom the set of threshold values. The image-processing device 102 may befurther configured to assign a second pre-defined binary value, such as“0”, to each of the one or more pixel locations, whose pixel values areless than the corresponding threshold values from the set of thresholdvalues.

In accordance with an embodiment, the image-processing device 102 may beconfigured to generate the plurality of Boolean maps for the pluralityof color channels and the set of threshold values. The generation may bebased on the assignment of the first and second pre-defined values. EachBoolean map from the plurality of Boolean maps may comprise one or moreregions. Each region from the one or more regions in a Boolean map mayinclude a group of neighbor pixels with similar pre-defined pixelvalues. Such one or more regions may correspond to the background orforeground objects.

In accordance with an embodiment, the image-processing device 102 may beconfigured to detect a plurality of regions in the plurality of Booleanmaps. The detected plurality of regions may be from the one or moreregions that touch one or more borders of the corresponding Boolean map.In accordance with an embodiment, the image-processing device 102 may beconfigured to detect another plurality of regions in the plurality ofBoolean maps. The detected other plurality of regions may not touch theone or more borders of the corresponding Boolean map. Theimage-processing device 102 may apply one or more morphologicaloperations known in the art, to identify such plurality of regions andother plurality of regions.

In accordance with an embodiment, the image-processing device 102 may beconfigured to determine the count of pixels of each region from theplurality of regions that touch the border of the plurality of Booleanmaps. The image-processing device 102 may be further configured todetermine a total count of pixels of each region from such plurality ofregions.

In accordance with an embodiment, the image-processing device 102 may beconfigured to determine a ratio of the determined count of pixels and asquare root of the total count of pixels of the corresponding region.The determined count of pixels may correspond to one or more pixels thattouch the border in each region from the plurality of regions in theplurality of Boolean maps. Based on the determined ratio, theimage-processing device 102 may be configured to computeboundary-connectedness values associated with each of the plurality ofregions in the plurality of Boolean maps.

In accordance with an embodiment, the boundary-connectedness value mayrepresent an extent to which a plurality of pixels present in thecorresponding region touches the one or more borders of the image orvideo frame. The image-processing device 102 may be configured tocompare each boundary-connectedness value of the plurality ofboundary-connectedness values with a pre-specified threshold value.

In accordance with an embodiment, the computed plurality ofboundary-connectedness values associated with some regions may be lessthan or equal to the pre-specified threshold value. Such regions may beidentified as a first set of regions that may correspond to a set offoreground objects that are required to be retained in the plurality ofBoolean maps. In accordance with an embodiment, the computed pluralityof boundary-connectedness values associated with other regions mayexceed the pre-specified threshold value. Such regions may be identifiedas a second set of regions that are required to be removed from theplurality of Boolean maps.

In accordance with an embodiment, the image-processing device 102 may beconfigured to remove the second set of regions as the correspondingboundary-connectedness values exceed the pre-specified threshold value.The image-processing device 102 may be further configured to retain thefirst set of regions with the corresponding boundary-connectednessvalues less than the pre-specified threshold value. The plurality ofBoolean maps which selectively comprise the retained first set ofregions may be hereinafter referred to as, “a plurality of processedBoolean maps”.

In accordance with an embodiment, the image-processing device 102 may beconfigured to add the plurality of processed Boolean maps. Theimage-processing device 102 may be further configured to normalize theadded plurality of processed Boolean maps by a value that corresponds toa total count of the plurality of Boolean maps.

In accordance with an embodiment, the image-processing device 102 maygenerate a saliency map. In accordance with an embodiment, thegeneration of saliency maps may include a post-processing of thenormalized plurality of processed Boolean maps, based on various imageprocessing techniques known in the art.

In accordance with an embodiment, the image-processing device 102 may beconfigured to detect a region-of-interest that corresponds to a salientregion in the generated saliency map. In accordance with an embodiment,the detection of the region-of-interest may be based on one or moresaliency parameters associated with the regions that correspond to thefirst set of regions identified as a set of foreground regions in theimage. The one or more saliency parameters may be based on intensityvalues of the detected salient regions that exceed a threshold intensityvalue. The image-processing device 102 may be further configured toadjust auto-focus on one or more salient objects that correspond to thedetected one or more salient regions.

FIG. 2 is a block diagram that illustrates various components of animage-processing device, in accordance with an embodiment of thedisclosure. With reference to FIG. 2, there is shown theimage-processing device 102, which may include a processor 202, animaging unit 204, a memory 206, an input/output (I/O) device 208, aBoolean map generation (BMG) unit 210, a boundary object removal (BOR)unit 212, a saliency map generation (SMG) unit 214, and a transceiver216. The I/O device 208 may further include the display screen 104. Inaccordance with an embodiment, the image-processing device 102 may becommunicatively coupled with the other units, such as the plurality ofcloud-based resources 106, via the communication network 108.

The processor 202 may comprise suitable logic, circuitry, interfaces,and/or code that may be configured to process an image or a video frame.The image or video frame may be processed for identification of a set offoreground regions that may touch one or more borders of the image orvideo frame. The processor 202 may be further configured to adjustauto-focus, based on one or more salient objects detected in theprocessed image or video frame. The processor 202 may be communicativelycoupled with the BMG unit 210, the BOR unit 212 and the SMG unit 214, toprocess the image or video frame. Examples of the processor 202 may bean X86-based processor, a Reduced Instruction Set Computing (RISC)processor, an Application-Specific Integrated Circuit (ASIC) processor,a Complex Instruction Set Computing (CISC) processor, a microcontroller,a central processing unit (CPU), a digital signal processor (DSP), agraphics processor unit (GPU), a coprocessor, and/or other processors orintegrated circuits.

The imaging unit 204 may comprise suitable logic, circuitry, interfaces,and/or code that may be configured to capture one or more images and/orvideo frames within a field-of-view (FOV) of the imaging unit 204. Theimaging unit 204 may further generate visual representations of thecaptured one or more images and/or video frames. The imaging unit 204may include a lens assembly and an image sensor that may enable theimaging unit 204 to capture the one or more images and/or video frames.The image sensor of the imaging unit 204 may be implemented by use of acharge-coupled device (CCD) technology, complementarymetal-oxide-semiconductor (CMOS) technology and/or the like.

The memory 206 may comprise suitable logic, circuitry, and/or interfacesthat may be configured to store a machine code and/or a computer programwith at least one code section executable by the processor 202. Inaccordance with an embodiment, the memory 206 may be further configuredto store the one or more images and/or video frames captured by theimaging unit 204. In accordance with an embodiment, the memory 206 maybe further configured to store one or more images and/or video framesreceived from the external unit, such as the plurality of cloud-basedresources 106, via the transceiver 216. Examples of types of the memory206 may include, but are not limited to, Random Access Memory (RAM),Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM),Thyristor Random Access Memory (T-RAM), Zero-Capacitor Random AccessMemory (Z-RAM), Read Only Memory (ROM), Hard Disk Drive (HDD), and/or aSecure Digital (SD) card, and/or cache memory.

The I/O device 208 may comprise suitable logic, circuitry, interfaces,and/or code that may be configured to receive an input from a user (notshown). The input from the user may correspond to a command to capturean image and/or a video frame of a scene. The I/O device 208 may befurther configured to provide an intermediate output and a final outputto the user. The intermediate output may correspond to visualization ofa saliency map. The final output may correspond to a visualization mayinclude one or more salient objects in the captured image and/or videoframe of the scene, on which the auto-focus is adjusted by the processor202. The I/O device 208 may comprise various input and output devicesthat may be configured to communicate with the processor 202. Examplesof the input devices may include, but are not limited to, the imagingunit 204, a camcorder, a touch screen, a keyboard, a mouse, a joystick,a microphone, a motion sensor, a light sensor, and/or a docking station.Examples of the output devices may include, but are not limited to, thedisplay screen 104, a projector screen, and/or a speaker. The displayscreen 104 may be further configured to display the one or more salientobjects, on which the auto-focus is adjusted by the processor 202. Thedisplay screen 104 may be configured to receive one or more inputactions from the one or more users, via a touch-sensitive screen. Suchone or more input actions may be received from the one or more users bymeans of a virtual keypad, a stylus, touch-based input actions, and/or agesture. The display screen 104 may be realized through several knowntechnologies such as, but not limited to, Liquid Crystal Display (LCD)display, Light Emitting Diode (LED) display, plasma display, and/orOrganic LED (OLED) display technology.

The BMG unit 210 may comprise suitable logic, circuitry, interfaces,and/or code that may be configured to compute the set of thresholdvalues for each color channel of the plurality of color channels in theimage or video frame. The BMG unit 210 may be further configured togenerate a Boolean map for each color channel of the plurality of colorchannels of the image and for each threshold value from the set ofthreshold values. The generation of Boolean map may be based on a set ofbinary values. The BMG unit 210 may be implemented, based on a number ofprocessor technologies known in the art.

The BOR unit 212 may comprise suitable logic, circuitry, interfaces,and/or code that may be configured to compute the plurality ofboundary-connectedness values associated with the plurality of regionsin the plurality of Boolean maps. Based on the computed plurality ofboundary-connectedness values, the BOR unit 212 may be configured togenerate a plurality of processed Boolean maps. The BOR unit 212 may beimplemented, based on a number of processor technologies known in theart.

The SMG unit 214 comprises suitable logic, circuitry, interfaces, and/orcode that may be configured to add the plurality of processed Booleanmaps. The SMG unit 214 may be further configured to normalize the addedplurality of processed Boolean maps by a value which corresponds to atotal count of Boolean maps. In accordance with an embodiment, the SMGunit 214 may be further configured to generate a saliency map, based onpost-processing of the normalized plurality of processed Boolean maps.The SMG unit 214 may be implemented, based on a number of processortechnologies known in the art.

The transceiver 216 may comprise suitable logic, circuitry, interfaces,and/or code that may be configured to transmit as well as receive theimage to/from the one or more communicatively coupled units. The one ormore communicatively coupled units may include the processor 202, theimaging unit 204, the memory 206, the I/O device 208, the BMG unit 210,the BOR unit 212, and/or the SMG unit 214. The transceiver 216 may beconfigured to communicate with the plurality of cloud-based resources106, via the communication network 108, as shown in FIG. 1. Thetransceiver 216 may be implemented by technologies known in the art, tosupport wired or wireless communication of the image-processing device102, with the communication network 108. Various components of thetransceiver 216 may include, but are not limited to, an antenna, a radiofrequency (RF) transceiver, one or more amplifiers, a tuner, one or moreoscillators, a digital signal processor, a coder-decoder (CODEC)chipset, a subscriber identity module (SIM) card, and/or a local buffer.

The transceiver 216 may communicate, via wireless communication, withnetworks (such as the Internet and/or the Intranet) and/or a wirelessnetwork (such as a cellular telephone network, a wireless local areanetwork (LAN) and/or a metropolitan area network (MAN)). The wirelesscommunication may use any of a plurality of communication standards,protocols and technologies, such as Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), widebandcode division multiple access (W-CDMA), code division multiple access(CDMA), time division multiple access (TDMA), Bluetooth, WirelessFidelity (Wi-Fi) (such as IEEE 802.11a, IEEE 802.11b, IEEE 802.11gand/or IEEE 802.11n), voice over Internet Protocol (VoIP), Wi-MAX, aprotocol for email, instant messaging, and/or Short Message Service(SMS).

In operation, the transceiver 216 in the image-processing device 102 maybe configured to receive an image from the imaging unit 204, via thecommunication network 108. The image may correspond to a scene of alandscape, a cityscape, a football match, a street-play, and/or thelike. In such a case, the imaging unit 204 may be configured to capturethe image in response to a request triggered by a user, based on anaction, such as hardware or software button-press action, at theimage-processing device 102.

In accordance with an embodiment, the transceiver 216 may be configuredto communicate the received image to the processor 202. In accordancewith an embodiment, the processor 202 may be configured to receive theimage from the memory 206. In such a case, the image may be temporarilystored in the memory 206. In accordance with an embodiment, theprocessor 202 may be configured to receive the image from the memory206. In accordance with an embodiment, the image may be of apre-specified resolution in the plurality of color channels. Inaccordance with an embodiment, the image may comprise background andforeground objects.

In accordance with an embodiment, the processor 202 may be configured toperform de-correlation of the plurality of color channels of the image,to reduce cross-correlation within the plurality of color channels. Thede-correlation may be performed by the processor 202, based on one ormore de-correlation techniques known in the art, such as a matchedlinear filter.

In accordance with an embodiment, the processor 202 may communicate theimage to the BMG unit 210. The BMG unit 210 may compute a count ofthreshold values for each color channel of the plurality of colorchannels in the image. In accordance with an embodiment, the computationof the count of threshold values may depend on one or more parameters.The one or more parameters may comprise a minimum pixel value, a maximumpixel value, and/or a step size. Values of the one or more parametersmay vary for each color channel of the plurality of color channels. Thestep size may be based on a count of bits that represent pixel values ofthe plurality of color channels. In accordance with an embodiment, thecount of threshold values may be expressed mathematically by equation(1a), as follows:

m=1+floor([max(u)−min(u)]/s)  (1a)

where “m” represents the count of threshold values, “max (u)” representsa maximum pixel value, “min (u)” represents a minimum pixel value, and“s” represents a pre-configured step size. The function floor(x) returnsthe largest integer value smaller than x, for example floor (1.6)=1.Further, the set of threshold values, “T”, may be determined based onthe count of threshold values, as determined based on the equation (1a),and the pre-configured step size, “s”. The set of threshold values, “T”,may be expressed mathematically by equation (1b), as follows:

T={0,s,2*s, . . . ,(m−1)*s}  (1b)

In accordance with an embodiment, the BMG unit 210 may be configured tocompare the pixel value of each pixel location with each threshold valuefrom the set of threshold values within the image. Based on thecomparison, the BMG unit 210 may be configured to assign a pre-specifiedbinary value, such as “0” or “1”, to each pixel location in the image.Accordingly, a first pre-defined binary value, such as “1”, may beassigned to each of the one or more pixel locations when pixel valuesexceed corresponding threshold value. Further, a second pre-definedbinary value, such as “0”, may be assigned to each of the one or morepixel locations when pixel values are less than the correspondingthreshold value.

The BMG unit 210 may be further configured to generate a plurality ofBoolean maps for each color channel and each threshold value of eachcolor channel, based on the assigned pre-specified binary values. EachBoolean map from the plurality of Boolean maps may comprise one or moreregions that correspond to background and foreground objects. Eachregion from the one or more regions in a Boolean map may include a groupof neighbor pixels with similar pre-defined pixel values. In accordancewith an embodiment, the comparison of the pixel value of each pixellocation with the set of threshold values and assignment of binaryvalues to each pixel may be mathematically expressed by equation (2), asfollows:

$\begin{matrix}{{b\left\lbrack {i,j} \right\rbrack}\left\{ \begin{matrix}{1,} & {{{if}{\mspace{14mu} \;}{u\left\lbrack {i,j} \right\rbrack}} > T_{h}} \\{0,} & {otherwise}\end{matrix} \right.} & (2)\end{matrix}$

where “T_(h)” represents threshold value from the set of thresholdvalues, “T”, “u [i, j]” represents pixel value at row “i” and column“j”, “b[i, j]” represents binary value assigned to pixel at row “i” andcolumn “j”, and “1” and “0” represents pre-specified binary values.

In accordance with an embodiment, the BMG unit 210 may be configured todetect a plurality of regions from the one or more regions that maytouch one or more borders of the corresponding Boolean map. Inaccordance with an embodiment, the BMG unit 210 may be configured todetect another plurality of regions in the plurality of Boolean maps.The detected other plurality of regions may not touch the one or moreborders of the corresponding Boolean map. The BMG unit 210 may apply oneor more morphological operations known in the art, to identify suchplurality of regions and other plurality of regions.

In accordance with an embodiment, the BMG unit 210 may communicate thegenerated plurality of Boolean maps and the detected plurality ofregions to the BOR unit 212. The BOR unit 212 may be configured tocompute a boundary-connectedness value of each region from the pluralityof regions associated with the plurality of Boolean maps. In accordancewith an embodiment, each of the plurality of boundary-connectednessvalues associated with the plurality of regions may be computed. Thecomputation may be based on a ratio of the count of pixels and a squareroot of total count of pixels of the corresponding region. The count ofpixels may correspond to one or more pixels that touch the border ineach region from the plurality of regions in the plurality of Booleanmaps. In accordance with an embodiment, the computation of theboundary-connectedness value may be mathematically expressed by equation(3), as follows:

$\begin{matrix}{{BC} = \frac{{Count}\mspace{14mu} {of}\mspace{11mu} {pixels}\mspace{14mu} {in}\mspace{14mu} R{\mspace{11mu} \;}{that}\mspace{14mu} {touch}\mspace{14mu} {the}\mspace{14mu} {border}}{\sqrt{{Total}{\mspace{11mu} \;}{count}\mspace{14mu} {of}\mspace{11mu} {pixels}\mspace{14mu} {in}\mspace{14mu} R}}} & (3)\end{matrix}$

where “BC” represents boundary-connectedness value and “R” representsregion in the Boolean map. Theoretically, the value of “BC” mayrepresent an extent to which a plurality of pixels present in thecorresponding region “R” touches the one or more borders of the Booleanmap.

The BOR unit 212 may be further configured to compare each of theplurality of boundary-connectedness values with a pre-specifiedthreshold value. In accordance with an embodiment, the plurality ofboundary-connectedness values associated with a set of regions may beless than or equal to the pre-specified threshold value. Such regionsmay be identified as a first set of regions that may correspond to a setof foreground objects that are required to be retained in the pluralityof Boolean maps. In accordance with an embodiment, the plurality ofboundary-connectedness values associated with another set of regions mayexceed the pre-specified threshold value. Such regions may be identifiedas a second set of regions that are required to be removed from theplurality of Boolean maps. Such second set of regions may correspond tothe non-salient foreground or background regions of the image.

In accordance with an embodiment, the BOR unit 212 may be configured toremove the second set of regions as the correspondingboundary-connectedness values exceed the pre-specified threshold value.The BOR unit 212 may be further configured to retain the first set ofregions with the corresponding boundary-connectedness values less thanthe pre-specified threshold value. Accordingly, the BOR unit 212 may beconfigured to generate a plurality of processed Boolean maps. The secondset of regions may be removed as the correspondingboundary-connectedness values exceed the pre-specified threshold value.The BOR unit 212 may be configured to generate a plurality of processedBoolean maps, as per the mathematical equation (4), as follows:

$\begin{matrix}{R = \left\{ \begin{matrix}{{0\mspace{14mu} ({removed})},} & {{{if}\mspace{14mu} {BC}} > \theta} \\{{1\mspace{14mu} ({retain})},} & {otherwise}\end{matrix} \right.} & (4)\end{matrix}$

where “R” represents a region in a Boolean map, “BC” represents theboundary-connectedness value of the region “R”, “θ” represents thepre-specified threshold value, and “1” and “0” represents pre-specifiedbinary values.

In accordance with an embodiment, the BOR unit 212 may be configured tocommunicate the plurality of Boolean maps to the SMG unit 214. Inaccordance with an embodiment, the SMG unit 214 may be configured to addthe plurality of processed Boolean maps. The SMG unit 214 may be furtherconfigured to normalize the added plurality of processed Boolean maps bya value that corresponds to a total count of Boolean maps. In accordancewith an embodiment, the normalization of the plurality of processedBoolean maps may be mathematically expressed by equation (5), asfollows:

$\begin{matrix}{B_{n} = {\frac{1}{n}{\sum\limits_{k = 1}^{n}B_{k}}}} & (5)\end{matrix}$

where “B_(n)” represents the normalized plurality of processed Booleanmaps, “B_(k)” represents k^(th) processed Boolean map, and “n”represents total count of Boolean maps.

In accordance with an embodiment, the SMG unit 214 may generate asaliency map. In accordance with an embodiment, the generation ofsaliency maps may include a post-processing of the normalized pluralityof processed Boolean maps. The post-processing of the normalizedplurality of processed Boolean maps may be based on various imageprocessing techniques known in the art. In accordance with anembodiment, the SMG unit 214 may be configured to detect aregion-of-interest that corresponds to a salient region in the generatedsaliency map. In accordance with an embodiment, the detection of theregion-of-interest may be based on one or more saliency parametersassociated with the regions that correspond to the first set of regionsidentified as a set of foreground regions in the image. The one or moresaliency parameters may be based on intensity values of the detectedsalient regions that exceed a threshold intensity value. The SMG unit214 may further identify at least one salient object that corresponds tothe detected region-of-interest in the generated saliency map. Theidentification of the salient object(s) may be incorporated in variousapplication fields, such as video surveillance, image retargeting, videosummarization, robot control, navigation assistance, object recognition,adaptive compression, and/or the like. The identification of the salientobjects may be further useful in image processing techniques (such asauto-focus algorithms), for an automatic detection of a focus area inthe captured image and/or the video frame.

FIG. 3 illustrates an exemplary scenario for implementation of thedisclosed method and system for image processing, in accordance with anembodiment of the disclosure. FIG. 3 is explained in conjunction withFIG. 1 and FIG. 2. With reference to FIG. 3, there is shown anarrangement 300 of various components of the image-processing device102, as described in FIG. 2. In accordance with such arrangement 300,there is shown a video frame 302 that may comprise a plurality ofregions. The plurality of regions may correspond to a plurality ofobjects, such as a first car 302 a, a second car 302 b, a third car 302c, a fourth car 302 d, a boundary slab 302 e, a first background region302 f 1, and a second background 302 f 2.

There is further shown a plurality of Boolean maps 304, such as a firstBoolean map 304 a, a second Boolean map 304 b, and a third Boolean map304 c. Each Boolean map of the plurality of Boolean maps 304 maycomprise a plurality of regions, based on a pre-specified thresholdvalue and pixel values and predominant color channels of correspondingobjects, such as the plurality of objects 302 a to 302 f 2. Inaccordance with the exemplary scenario, such plurality of regions may beselectively shown in each of the plurality of Boolean maps 304. Forexample, the first Boolean map 304 a may include regions 304 d and 304e. The region 304 d collectively corresponds to all regions (removedregions) in the video frame 302 except the second car 302 b. The region304 e corresponds to the second car 302 b. In addition to the region 304e, the second Boolean map 304 b may further include regions 304 f and304 g that correspond to the first car 302 a and the fourth car 302 d,respectively. The new region 304 d 1 in the second Boolean mapcorresponds to all regions (removed regions) in the video frame 302except the cars 302 a, 302 b, and 302 d. The third Boolean map 304 c mayinclude the regions 304 e, 304 f, 304 g, 304 h, and 304 d 2 thatcorrespond to the second car 302 b, the first car 302 a, the fourth car302 d, the boundary slab 302 e, and the first background region 302 f 1,respectively. It may also include region 304 d 3 that corresponds to thethird car 302 c and the second background region 302 f 2.

There is further shown a plurality of processed Boolean maps 306 thatmay include a first processed Boolean map 306 a, a second processedBoolean map 306 b, and a third processed Boolean map 306 c. Theplurality of processed Boolean maps 306 may include the plurality ofregions that correspond to the plurality of objects, as discussed above.The plurality of regions in the plurality of processed Boolean maps 306may further include regions 306 e, 306 f, and 306 g, that corresponds tothe objects, such as the second car 302 b, the first car 302 a, and theboundary slab 302 e, respectively. The plurality of regions in theplurality of processed Boolean maps 306 may not include a regions thatcorresponds to the fourth car 302 d, the first background region 302 f1, and the second background region 302 f 2. There is further shown afinal saliency map 308 that may comprise regions-of-interest, such asregions 308 a and 308 b, which correspond to the first car 302 a and thesecond car 302 b, respectively.

In accordance with the exemplary scenario, the transceiver 216 in theimage-processing device 102 may be configured to receive a video streamfrom the imaging unit 204, via the communication network 108. The videostream may include a plurality of video frames, such as the video frame302. In such a case, the imaging unit 204 may be configured to capturethe video stream in response to a request triggered by a user, based onan action, such as hardware or software button-press action. The videoframe 302 may correspond to a video stream of car rally. The video frame302 may comprise a plurality of regions. The plurality of regions maycorrespond to a plurality of objects, such as the first car 302 a, thesecond car 302 b, the third car 302 c, the fourth car 302 d, theboundary slab 302 e, the first background region 302 f 1, and the secondbackground region 302 f 2.

The transceiver 216 may be configured to communicate the received videoframe 302 to the processor 202. In accordance with an embodiment, theprocessor 202 may be configured to identify the plurality of objects asbackground and foreground objects. In accordance with an embodiment, theprocessor 202 may be configured to perform de-correlation of theplurality of color channels of the video frame 302 to reducecross-correlation within the plurality of color channels, based on oneor more de-correlation techniques known in the art, such as a matchedlinear filter.

In accordance with an embodiment, the processor 202 may communicate thevideo frame 302 to the BMG unit 210. The BMG unit 210 may compute acount of threshold values for each color channel of the plurality ofcolor channels in the video frame 302. The computation of the count ofthreshold values may depend on one or more parameters, such as a minimumpixel value, a maximum pixel value, and a step size. The computation ofthe count of threshold values and the set of threshold values may beperformed based on the equations (1a) and (1b), as described in FIG. 2.

The BMG unit 210 may be further configured to compare the pixel value ofeach pixel location with each threshold value from the set of thresholdvalues within the video frame 302. Based on the comparison, the BMG unit210 may be configured to assign a first pre-defined binary value, suchas “1”, to each of the one or more pixel locations with pixel valuesthat exceed the corresponding threshold value. Further, the BMG unit 210may be configured to assign a second pre-defined binary value, such as“0”, to each of the one or more pixel locations with pixel values thatare less than the corresponding threshold value. The comparison of thepixel value of each pixel location with the set of threshold values andassignment of binary values to each pixel may be performed based on themathematically expressed equation (2), as described in FIG. 2. The BMGunit 210 may generate the plurality of Boolean maps 304 for each colorchannel of the plurality of color channels of the video frame 302 andeach threshold value from the set of threshold values, based on the setof binary values.

In accordance with an embodiment, the BMG unit 210 may determine one ormore regions 304 d to 304 h, 304 d 1, 304 d 2, and 304 d 3 in theplurality of Boolean maps 304, as described above. In accordance with anembodiment, the BMG unit 210 may be further configured to remove aplurality of regions from the plurality of Boolean maps 304 thatcorresponds to one or more objects, such as third car 302 c. Theplurality of regions may be removed as such one or more objects maycomprise a plurality of pixels with pixel intensity value similar to thefirst background region 302 f 1 and the second background region 302 f2. The BMG unit 210 may be configured to detect a plurality of regions(from the one or more regions 304 d to 304 h, 304 d 1, 304 d 2, and 304d 3) that may touch the borders of the video frame 302. Such pluralityof regions may correspond to the first car 302 a, the second car 302 b,the fourth car 302 d, the boundary slab 302 e, the first backgroundregion 302 f 1, and the second background region 302 f 2. The BMG unit210 may be further configured to detect a plurality of regions (notshown) that does not touch the borders of the video frame 302, and thus,may be primarily a foreground region.

The BMG unit 210 may communicate the generated plurality of Boolean maps304, and the plurality of determined regions 304 d to 304 h, 304 d 1,304 d 2, and 304 d 3 to the BOR unit 212. Accordingly, the BOR unit 212may compute the plurality of boundary-connectedness values associatedwith the regions 304 d to 304 h, 304 d 1, 304 d 2, and 304 d 3 in theplurality of Boolean maps 304. Each of the plurality ofboundary-connectedness values associated with the plurality of regions,such as the regions 304 d to 304 h, 304 d 1, 304 d 2, and 304 d 3, maybe computed based on a ratio of count of pixels of corresponding regionand a square root of total count of pixels of the corresponding region.The determined count of pixels may correspond to one or more pixels thattouch the border in each region from the plurality of regions in theplurality of Boolean maps. Such computation of theboundary-connectedness values may be performed based on themathematically expressed equation (3), as described in FIG. 2.

Further, the BOR unit 212 may compare each boundary-connectedness valuewith a pre-specified threshold value from the set of threshold values.In an instance, a boundary-connectedness value of a region, such as theregion that corresponds to the first car 302 a, is less than thepre-specified threshold value. Similarly, the boundary-connectednessvalues of the regions that corresponds to the second car 302 b and theboundary slab 302 f, are less than the pre-specified threshold value.Such regions, depicted by regions 306 f, 306 e, and 306 g, respectively,may be identified as a first set of regions. In such a case, the BORunit 212 may retain each region from the identified first set ofregions, in the generated plurality of processed Boolean maps 306. Inanother instance, a boundary-connectedness values of regions thatcorrespond to the fourth car 302 d, the first background region 302 f 1,and the second background region 302 f 2 together with the third car 302c, exceeds the pre-specified threshold value. Such a region may beidentified as a second set of regions. In such a case, the BOR unit 212may generate the plurality of processed Boolean maps 306, without theregions that correspond to the fourth car 302 d, the first backgroundregion 302 f 1, the second background region 302 f 2, and the third car302 c.

The BOR unit 212 may be configured to communicate the plurality ofprocessed Boolean maps 306 to the SMG unit 214. The SMG unit 214 may addthe plurality of processed Boolean maps 306 together. Further the SMGunit 214 may normalize the added plurality of processed Boolean maps 306by a value that corresponds to a total count of Boolean maps, such as“3”. The normalization of the added plurality of processed Boolean maps306 may be mathematically expressed by the equation (5), as described inFIG. 2. Based on the normalization, the SMG unit 214 may generate thesaliency map 308, by use of one or more image processing techniquesknown in the art. The saliency map 308 may include a plurality ofsalient regions, such as the regions 308 a and 308 b.

The SMG unit 214 may be configured to detect a region-of-interest thatcorresponds to a salient region, such as the salient region 308 b, fromplurality of regions-of-interest in the generated saliency map 308. Sucha detection of the region-of-interest may be based on one or moresalient parameters, such as high intensity value, bright color and highspeed of the first car 302 b. Accordingly, the processor 202 mayautomatically focus on the salient object, such as the second car 302 b,in the captured video frame 302.

FIG. 4 illustrates a flow chart for implementation of an exemplarymethod for image processing, in accordance with an embodiment of thedisclosure. FIG. 4 is described in conjunction with elements of FIGS. 1,2, and 3. The method, in accordance with a flowchart 400 in FIG. 4, maybe implemented in the image-processing device 102. The image-processingdevice 102 may be communicatively coupled with the plurality ofcloud-based resources 106, as described in FIG. 1.

With reference to FIG. 4, the method, in accordance with the flowchart400, begins at step 402 and proceeds to step 404. At step 404, an imageor a video frame, such as the video frame 302, captured by the imagingunit 204, may be received by the processor 202. The video frame 302 mayinclude a plurality of color channels. At step 404, a de-correlation ofthe plurality of color channels of the video frame 302 may be optionallyperformed, based on a whitening operation. The de-correlation of theplurality of color channels in the video frame 302 may be performed toreduce a cross-correlation within the plurality of color channels. Inaccordance with an embodiment, the de-correlation of the plurality ofcolor channels of the video frame 302 may not be performed. At step 406,a set of threshold values for each color channel of the plurality ofcolor channels in the image may be computed. Such a computation may beperformed by the BMG unit 210, in conjunction with the processor 202.The BMG unit 210, in conjunction with the processor 202, may assign apre-specified binary value to each pixel location of each region in thevideo frame 302. Such an assignment of the pre-specified binary valuesmay be based on a comparison of the pixel values at each pixel location,with a threshold value for each of the set of threshold values.

At step 408, a plurality or stack, of Boolean maps 304 for each of thecomputed set of threshold values and each color channels of the one ormore channels may be generated by the BMG unit 210. The plurality ofBoolean maps 304 may be generated based on the binary values assigned toeach pixel location of each region in the video frame 302.

At step 410, a boundary-connectedness value for each region in theplurality of Boolean maps 304 may be computed. Such a computation may beperformed by the BOR unit 212, in conjunction with the processor 202. Inaccordance with an embodiment, the computation of theboundary-connectedness value may be based on a ratio of total count ofpixels of the region that touch the border of the Boolean map and asquare root of total count of pixels in the region, as described in FIG.2.

At step 412, it may be determined whether the boundary-connectednessvalue of each region in the Boolean map exceeds a pre-specifiedthreshold value. For instance, when the boundary-connectedness value ofa region is less than or equal to the pre-specified threshold value, thecontrol passes to step 414 a. In another instance, when theboundary-connectedness value of the region exceeds the pre-specifiedthreshold value, the control passes to step 414 b.

At step 414 a, the region may be identified as one of the first set ofregions and subsequently, at step 416 a, the first set of regions may beretained in the Boolean map by the BOR unit 212 for further processing.Control passes to step 418. At step 414 b, the region may be identifiedas one of the second set of regions and at step 416 b, the second set ofregions may be removed from the Boolean map by the BOR unit 212 and maynot be further processed. Control passes to step 418.

At step 418, a processed Boolean map may be generated by the BOR unit212, based on the retained first set of regions. At step 420, theprocessor 202 checks for next Boolean map to be processed. In aninstance, when there are more Boolean maps to be processed, the controlpasses back to 410. In another instance, when there are no more Booleanmaps left to be processed, the control passes to step 422.

At step 422, the plurality of processed Boolean maps may be added by theSMG unit 214. At step 424, the added plurality of processed Boolean maps306 may be normalized by a value that corresponds to a total count ofthe plurality of Boolean maps 304. The normalization may be performed bythe SMG unit 214. At step 426, a saliency map, such as the saliency map308, may be generated by the SMG unit 214. The saliency map 308 may begenerated, based on post-processing of the plurality of normalizedplurality of processed Boolean maps and one or more saliency parameters.The SMG unit 214 may be configured to detect a region-of-interest thatcorresponds to a salient region, such as the salient region 308 a, fromplurality of regions-of-interest in the generated saliency map 308. Sucha detection of the region-of-interest may be based on one or moresalient parameters, such as higher intensity of the detected salientregions. Accordingly, the processor 202 may automatically focus on thesalient object, such as the first car 302 a, in the captured video frame302. Control passes to end step 428.

In accordance with an embodiment of the disclosure, a system to processan image is disclosed. The system may comprise an image-processingdevice 102 (FIG. 1), which may be configured to generate a saliency mapof an image. The image-processing device 102 may comprise one or morecircuits (or processors), such as the processor 202, the BMG unit 210,the BOR unit 212, and the SMG unit 214 (FIG. 2). The BMG unit 210 may beconfigured to compute a plurality of boundary-connectedness valuesassociated with one or more regions in a plurality of Boolean maps.Further, the plurality of Boolean maps may correspond to a plurality ofcolor channels of an image. The BOR unit 212 may be further configuredto compare the plurality of boundary-connectedness values associatedwith the one or more regions in the plurality of Boolean maps with apre-specified threshold value. The BOR unit 212 may be configured toidentify at least a first set of regions of the plurality of regions inthe plurality of Boolean maps as a set of foreground regions, based onthe comparison.

Various embodiments of the disclosure may provide a non-transitorycomputer readable medium and/or storage medium, wherein there is storedthereon, a machine code and/or a computer program with at least one codesection executable by a machine and/or a computer to process an image.The at least one code section may cause the machine and/or computer toperform the steps that comprise the generation of a saliency map of animage. A plurality of boundary-connectedness values may be computed,which may be associated with a plurality of regions in a plurality ofBoolean maps. The plurality of boundary-connectedness values associatedwith the plurality of regions may be compared with a pre-specifiedthreshold value. At least a first set of regions of the plurality ofregions in the plurality of Boolean maps may be identified as a set offoreground regions, based on the comparison.

The present disclosure may be realized in hardware, or a combination ofhardware and software. The present disclosure may be realized in acentralized fashion, in at least one computer system, or in adistributed fashion, where different elements may be spread acrossseveral interconnected computer systems. A computer system or otherapparatus adapted to carry out the methods described herein may besuited. A combination of hardware and software may be a general-purposecomputer system with a computer program that, when loaded and executed,may control the computer system such that it carries out the methodsdescribed herein. The present disclosure may be realized in hardwarethat comprises a portion of an integrated circuit that also performsother functions.

The present disclosure may also be embedded in a computer programproduct, which comprises all the features that enable the implementationof the methods described herein, and which when loaded in a computersystem is able to carry out these methods. Computer program, in thepresent context, means any expression, in any language, code ornotation, of a set of instructions intended to cause a system with aninformation processing capability to perform a particular functioneither directly, or after either or both of the following: a) conversionto another language, code or notation; b) reproduction in a differentmaterial form.

While the present disclosure has been described with reference tocertain embodiments, it will be understood by those skilled in the artthat various changes may be made and equivalents may be substitutedwithout departure from the scope of the present disclosure. In addition,many modifications may be made to adapt a particular situation ormaterial to the teachings of the present disclosure without departurefrom its scope. Therefore, it is intended that the present disclosurenot be limited to the particular embodiment disclosed, but that thepresent disclosure will include all embodiments that fall within thescope of the appended claims.

What is claimed is:
 1. A method for image processing, said methodcomprising: computing, by an image-processing device, a plurality ofboundary-connectedness values associated with a plurality of regions ina plurality of Boolean maps, wherein said plurality of Boolean mapscorresponds to a plurality of color channels of an image; comparing, bysaid image-processing device, said plurality of boundary-connectednessvalues associated with said plurality of regions with a pre-specifiedthreshold value; and identifying, by said image-processing device, atleast a first set of regions of said plurality of regions in saidplurality of Boolean maps as a set of foreground regions based on saidcomparison.
 2. The method according to claim 1, wherein each of saidplurality of boundary-connectedness values associated with saidplurality of regions is computed based on a ratio of count of pixels ofa corresponding region that touches one or more borders of saidplurality of Boolean maps, and a square root of total count of pixels ofsaid corresponding region.
 3. The method according to claim 1, whereinsaid image is a de-correlated image that includes one or more regionscorresponding to background or foreground objects, wherein saidplurality of regions of said one or more regions touches one or moreborders of said image, wherein another plurality of regions of said oneor more regions does not touch said one or more borders of said image.4. The method according to claim 1, further comprising generating, bysaid image-processing device, said plurality of Boolean maps for eachcolor channel of said plurality of color channels of said image and eachthreshold value of a set of threshold values, based on a set of binaryvalues, wherein said set of binary values are determined based on acomparison of pixel intensity with said threshold value at each pixellocation of said image.
 5. The method according to claim 4, furthercomprising computing, by said image-processing device, said set ofthreshold values of each color channel of said plurality of colorchannels based on a step size, a minimum pixel value, and a maximumpixel value of a corresponding color channel.
 6. The method according toclaim 5, wherein said step size is based on a count of bits thatrepresents pixel values of said plurality of color channels.
 7. Themethod according to claim 1, further comprising identifying, by saidimage-processing device, a second set of regions of said plurality ofregions as a set of background regions, based on said comparison,wherein said identified first set of regions are retained and saididentified second set of regions are removed from said plurality ofBoolean maps to generate a plurality of processed Boolean maps.
 8. Themethod according to claim 7, wherein one or more boundary-connectednessvalues associated with said first set of regions is less than or equalto said pre-specified threshold value, wherein one or moreboundary-connectedness values associated with said second set of regionsexceed said pre-specified threshold value.
 9. The method according toclaim 7, further comprising generating, by said image-processing device,a saliency map, wherein said generation of saliency map is based onaddition of said plurality of processed Boolean maps and normalizationof said added said plurality of processed Boolean maps.
 10. The methodaccording to claim 9, further comprising detecting, by saidimage-processing device, a region-of-interest that corresponds to asalient region in said generated saliency map.
 11. The method accordingto claim 10, wherein said detection of said region-of-interest is basedon one or more saliency parameters corresponding to said first set ofregions identified as a set of foreground regions in said image, whereinsaid one or more saliency parameters are based on intensity values ofsaid detected salient regions that exceed a threshold saliency value.12. A system for image processing, said system comprising: one or morecircuits in an image-processing device, said one or more circuits beingconfigured to: compute a plurality of boundary-connectedness valuesassociated with a plurality of regions in a plurality of Boolean maps,wherein said plurality of Boolean maps corresponds to a plurality ofcolor channels of an image; compare said plurality ofboundary-connectedness values associated with said plurality of regionswith a pre-specified threshold value; and identify at least a first setof regions from said plurality of regions in said plurality of Booleanmaps as a set of foreground regions based on said comparison.
 13. Thesystem according to claim 12, wherein each of said plurality ofboundary-connectedness values associated with said plurality of regionsis computed based on a ratio of count of pixels of a correspondingregion touching one or more borders of said plurality of Boolean mapsand a square root of total count of pixels of said corresponding region.14. The system according to claim 12, wherein said one or more circuitsare configured to generate said plurality of Boolean maps for each colorchannel of said plurality of color channels of said image and eachthreshold value of a set of threshold values, based on a set of binaryvalues, wherein said set of binary values are determined based on acomparison of pixel intensity with said threshold value at each pixellocation of said image.
 15. The system according to claim 14, whereinsaid one or more circuits are configured to compute said set ofthreshold values of each color channel of said plurality of colorchannels based on a step size, a minimum pixel value and a maximum pixelvalue of a corresponding color channel, wherein said step size is basedon a count of bits that represent pixel values of said plurality ofcolor channels.
 16. The system according to claim 12, wherein said oneor more circuits are configured to identify a second set of regions ofsaid plurality of regions as a set of background regions, based on saidcomparison, wherein said identified first set of regions are retainedand said identified second set of regions are removed from saidplurality of Boolean maps to generate a plurality of processed Booleanmaps.
 17. The system according to claim 16, wherein one or moreboundary-connectedness values associated with said first set of regionsis less than or equal to said pre-specified threshold value, wherein oneor more boundary-connectedness values associated with said second set ofregions exceed said pre-specified threshold value.
 18. The systemaccording to claim 17, wherein said one or more circuits are configuredto generate a saliency map, wherein said generation of saliency mapcomprises addition of said plurality of processed Boolean maps andnormalization of said added said plurality of processed Boolean maps.19. The system according to claim 18, wherein said one or more circuitsare configured to detect a region-of-interest that corresponds to asalient region in said generated saliency map.
 20. The system accordingto claim 19, wherein said detection of said region-of-interest is basedon one or more saliency parameters corresponding to said first set ofregions identified as a set of foreground regions in said image, whereinsaid one or more saliency parameters are based on intensity values ofsaid detected salient regions that exceed a threshold intensity value.21. A non-transitory computer-readable storage medium having storedthereon, a set of computer-executable instructions for causing acomputer comprising one or more processors to perform steps comprising:computing, by an image-processing device, a plurality ofboundary-connectedness values associated with a plurality of regions ina plurality of Boolean maps, wherein said plurality of Boolean mapscorrespond to a plurality of color channels of an image; comparing, bysaid image-processing device, said plurality of boundary-connectednessvalues associated with said plurality of regions with a pre-specifiedthreshold value; and identifying, by said image-processing device, afirst set of regions from said plurality of regions in said plurality ofBoolean maps as foreground regions based on said comparison.